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Across

1. The simulation of human intelligence processes by computer systems.

2. A type of machine learning algorithm that uses principles of evolution to generate solutions to complex problems.

3. A field of artificial intelligence that enables computers to interpret and understand the visual world, including images and videos.

4. A method of further training a pre-trained model on a specific dataset to improve performance on a particular task.

5. A field of study that uses statistical algorithms to enable a machine to improve its performance on a specific task.

6. As AI becomes more integrated into various domains, there is a risk of over-reliance and reduced human autonomy.

7. An optimization algorithm that mimics the process of natural selection.

8. Explainability is the ability to provide understandable explanations or justifications for the decisions and outcomes generated by an AI system.

9. The reduction in expenses or overhead through the implementation of AI.

10. A type of machine learning that involves training a system through trial-and-error using feedback from its environment.

Down

11. Machine learning models that can generate new and original content, such as images, texts, or music.

12. The process of breaking text into smaller pieces, called tokens, which can be words or subwords.

13. A type of artificial intelligence that attempts to simulate human thought processes and decision-making.

14. A type of machine learning algorithm that uses layers of neural networks to learn and make predictions from complex data sets.

15. AI systems can be vulnerable to hacking and manipulation, leading to potential security breaches and misuse of information.

16. AI systems often face ethical dilemmas where they have to make decisions that may have moral implications, raising concerns about accountability and responsibility.

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